




"""simple docstring"""

import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
a							      =     logging.getLogger(__name__)






def 		_snake_case	(   _snake_case				:   str				)       ->   int:






      '''simple docstring'''


      _A     =  git.Repo(search_parent_directories=_snake_case				)
      _A     =  {
          'repo_id': str(_snake_case				),
          'repo_sha': str(repo.head.object.hexsha				),
          'repo_branch': str(repo.active_branch				),
      }

      with open(os.path.join(_snake_case							,    'git_log.json'				)							,    'w'				) as f:
            json.dump(_snake_case							,    _snake_case							,    indent=4				)






def 		_snake_case	(   _snake_case				:   List[str]				)       ->   str:






      '''simple docstring'''


      if params.n_gpu <= 0:
            _A     =  0
            _A     =  -1
            _A     =  True
            _A     =  False
            return

      assert torch.cuda.is_available()

      logger.info('Initializing GPUs'				)
      if params.n_gpu > 1:
            assert params.local_rank != -1

            _A     =  int(os.environ['WORLD_SIZE']				)
            _A     =  int(os.environ['N_GPU_NODE']				)
            _A     =  int(os.environ['RANK']				)

            # number of nodes / node ID
            _A     =  params.world_size // params.n_gpu_per_node
            _A     =  params.global_rank // params.n_gpu_per_node
            _A     =  True

            assert params.n_nodes == int(os.environ['N_NODES']				)
            assert params.node_id == int(os.environ['NODE_RANK']				)

      # local job (single GPU)
      else:
            assert params.local_rank == -1

            _A     =  1
            _A     =  0
            _A     =  0
            _A     =  0
            _A     =  1
            _A     =  1
            _A     =  False

      # sanity checks
      assert params.n_nodes >= 1
      assert 0 <= params.node_id < params.n_nodes
      assert 0 <= params.local_rank <= params.global_rank < params.world_size
      assert params.world_size == params.n_nodes * params.n_gpu_per_node

      # define whether this is the master process / if we are in multi-node distributed mode
      _A     =  params.node_id == 0 and params.local_rank == 0
      _A     =  params.n_nodes > 1

      # summary
      _A     =  F'''--- Global rank: {params.global_rank} - '''
      logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes				)
      logger.info(PREFIX + 'Node ID        : %i' % params.node_id				)
      logger.info(PREFIX + 'Local rank     : %i' % params.local_rank				)
      logger.info(PREFIX + 'World size     : %i' % params.world_size				)
      logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node				)
      logger.info(PREFIX + 'Master         : %s' % str(params.is_master				)				)
      logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node				)				)
      logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu				)				)
      logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()				)

      # set GPU device
      torch.cuda.set_device(params.local_rank				)

      # initialize multi-GPU
      if params.multi_gpu:
            logger.info('Initializing PyTorch distributed'				)
            torch.distributed.init_process_group(
                init_method='env://'							,    backend='nccl'							,    )







def 		_snake_case	(   _snake_case				:   Any				)       ->   Union[str, Any]:






      '''simple docstring'''


      np.random.seed(args.seed				)
      torch.manual_seed(args.seed				)
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed				)









"""simple docstring"""





import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMSNForImageClassification, ViTMSNModel
  from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class 	a		:






    def __init__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str=13			,		__SCREAMING_SNAKE_CASE	:				int=30			,		__SCREAMING_SNAKE_CASE	:				List[Any]=2			,		__SCREAMING_SNAKE_CASE	:				Dict=3			,		__SCREAMING_SNAKE_CASE	:				List[str]=True			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				Any=32			,		__SCREAMING_SNAKE_CASE	:				List[str]=5			,		__SCREAMING_SNAKE_CASE	:				List[str]=4			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=37			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="gelu"			,		__SCREAMING_SNAKE_CASE	:				List[Any]=0.1			,		__SCREAMING_SNAKE_CASE	:				Tuple=0.1			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=10			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.02			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=None			,		)	->						List[str]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		patch_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		intermediate_size
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		hidden_dropout_prob
      lowerCamelCase_       	=		attention_probs_dropout_prob
      lowerCamelCase_       	=		type_sequence_label_size
      lowerCamelCase_       	=		initializer_range
      lowerCamelCase_       	=		scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      lowerCamelCase_       	=		(image_size // patch_size) ** 2
      lowerCamelCase_       	=		num_patches + 1






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      lowerCamelCase_       	=		None
      if self.use_labels:
        lowerCamelCase_       	=		ids_tensor([self.batch_size]			,		self.type_sequence_label_size   )

      lowerCamelCase_       	=		self.get_config()

      return config, pixel_values, labels






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Union[str, Any]:
      return ViTMSNConfig(
          image_size=self.image_size			,		patch_size=self.patch_size			,		num_channels=self.num_channels			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		initializer_range=self.initializer_range			,		)






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Any:
      lowerCamelCase_       	=		ViTMSNModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				str   )	->						List[str]:
      lowerCamelCase_       	=		self.type_sequence_label_size
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE   )
      print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'   )
      print('Labels: {labels}'   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )

      # test greyscale images
      lowerCamelCase_       	=		1
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()

      lowerCamelCase_       	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						Tuple:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 	a		(   __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	str		=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	Dict		=					(
        {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE		:	Dict		=					False
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					False
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					False
    SCREAMING_SNAKE_CASE		:	str		=					False






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[str]:
      lowerCamelCase_       	=		ViTMSNModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		has_text_modality=__SCREAMING_SNAKE_CASE			,		hidden_size=37   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      self.config_tester.run_common_tests()






    @unittest.skip(reason='ViTMSN does not use inputs_embeds'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Union[str, Any]:
      pass






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[Any]:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(model.get_input_embeddings()			,		(nn.Module)   )
        lowerCamelCase_       	=		model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE			,		nn.Linear   )   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase_       	=		[*signature.parameters.keys()]

        lowerCamelCase_       	=		['pixel_values']
        self.assertListEqual(arg_names[:1]			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[int]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_       	=		ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				) ->							List[Any]:
  lowerCamelCase_       	=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image






@require_torch
@require_vision
class 	a		(   unittest.TestCase						):






    @cached_property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'   ) if is_vision_available() else None






    @slow
    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      torch.manual_seed(2   )
      lowerCamelCase_       	=		ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'   ).to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.default_image_processor
      lowerCamelCase_       	=		prepare_img()
      lowerCamelCase_       	=		image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   ).to(__SCREAMING_SNAKE_CASE   )

      # forward pass
      with torch.no_grad():
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

      # verify the logits
      lowerCamelCase_       	=		torch.Size((1, 1000)   )
      self.assertEqual(outputs.logits.shape			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor([-0.0_803, -0.4_454, -0.2_375]   ).to(__SCREAMING_SNAKE_CASE   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		__SCREAMING_SNAKE_CASE			,		atol=1e-4   )   )




